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Found 10 papers, 2 papers with code
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings.
Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Scoliosis is a congenital disease that causes lateral curvature in the spine.
Partly Supervised Multitask Learning
Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.
Analysis of Scoliosis From Spinal X-Ray Images
Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.
Active Image Synthesis for Efficient Labeling
The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions.
